Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation
This article proposes a knowledge-driven four-wave mixing (FWM) mitigation using supervised learning approaches and a multilevel regression-based dense wavelength division multiplexing (SMR-DWDM) system design. The evolution of 5G and the Internet of Things (IoT) results in an immense data rate cons...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10776958/ |
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| author | Sudha Sakthivel Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su'ud Mohammad Riyaz Belgaum |
| author_facet | Sudha Sakthivel Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su'ud Mohammad Riyaz Belgaum |
| author_sort | Sudha Sakthivel |
| collection | DOAJ |
| description | This article proposes a knowledge-driven four-wave mixing (FWM) mitigation using supervised learning approaches and a multilevel regression-based dense wavelength division multiplexing (SMR-DWDM) system design. The evolution of 5G and the Internet of Things (IoT) results in an immense data rate consumption and introduces unprecedented dynamic network traffic. DWDM networks effectively accommodate these challenges by being highly responsive and adaptable to changes in traffic impact and network conditions. High-capacity DWDM transmission causes fiber nonlinearities, reducing system performance and effective bandwidth utilization and affecting Quality of Transmission (QoT) by inducing crosstalk, dispersion, and Inter-Symbol Interference (ISI). This work discusses knowledge-driven DWDM design, utilizing machine learning to improve flexibility, identify FWM parameters, and predict transmission quality. Firstly, machine learning optimizes parameters at the transmitter end to identify FWM monitoring factors, predict QoT based on subscriber requirements, and create a comprehensive database for training Machine Learning (ML) models. Then, supervised multilevel regression builds the knowledge-driven QoT Estimator, accurately selecting input parameter combinations for the automatic monitoring controller of the DWDM system. The accuracy of the proposed SMR-DWDM system is confirmed by validating it with various FWM mitigating factors monitored by Optical Spectrum Analyzer (OSA) and Bit Error Rate (BER) analyzers. Through parametric analysis and supervised multilevel regression, the system achieves high precision and accurately predicts QoT by over 80%, and improves 25% of the QoT enhancement compared with traditional methods, proving its effectiveness in managing fiber nonlinearities. |
| format | Article |
| id | doaj-art-196f44f8fedc47cb835b4f1e952b40e5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-196f44f8fedc47cb835b4f1e952b40e52024-12-20T00:00:52ZengIEEEIEEE Access2169-35362024-01-011219065019066510.1109/ACCESS.2024.351069610776958Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM MitigationSudha Sakthivel0https://orcid.org/0009-0002-4234-7080Muhammad Mansoor Alam1https://orcid.org/0000-0001-5773-7140Aznida Abu Bakar Sajak2https://orcid.org/0000-0003-2475-1047Mazliham Mohd Su'ud3https://orcid.org/0000-0001-9975-4483Mohammad Riyaz Belgaum4https://orcid.org/0000-0002-6155-1530Advanced Telecommunication Technology Research Cluster, Universiti Kuala Lumpur, Kuala Lumpur, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaAdvanced Telecommunication Technology Research Cluster, Universiti Kuala Lumpur, Kuala Lumpur, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya, MalaysiaThis article proposes a knowledge-driven four-wave mixing (FWM) mitigation using supervised learning approaches and a multilevel regression-based dense wavelength division multiplexing (SMR-DWDM) system design. The evolution of 5G and the Internet of Things (IoT) results in an immense data rate consumption and introduces unprecedented dynamic network traffic. DWDM networks effectively accommodate these challenges by being highly responsive and adaptable to changes in traffic impact and network conditions. High-capacity DWDM transmission causes fiber nonlinearities, reducing system performance and effective bandwidth utilization and affecting Quality of Transmission (QoT) by inducing crosstalk, dispersion, and Inter-Symbol Interference (ISI). This work discusses knowledge-driven DWDM design, utilizing machine learning to improve flexibility, identify FWM parameters, and predict transmission quality. Firstly, machine learning optimizes parameters at the transmitter end to identify FWM monitoring factors, predict QoT based on subscriber requirements, and create a comprehensive database for training Machine Learning (ML) models. Then, supervised multilevel regression builds the knowledge-driven QoT Estimator, accurately selecting input parameter combinations for the automatic monitoring controller of the DWDM system. The accuracy of the proposed SMR-DWDM system is confirmed by validating it with various FWM mitigating factors monitored by Optical Spectrum Analyzer (OSA) and Bit Error Rate (BER) analyzers. Through parametric analysis and supervised multilevel regression, the system achieves high precision and accurately predicts QoT by over 80%, and improves 25% of the QoT enhancement compared with traditional methods, proving its effectiveness in managing fiber nonlinearities.https://ieeexplore.ieee.org/document/10776958/FWMmachine learningknowledge-driven DWDMmultilevel regressionquality of transmission |
| spellingShingle | Sudha Sakthivel Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su'ud Mohammad Riyaz Belgaum Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation IEEE Access FWM machine learning knowledge-driven DWDM multilevel regression quality of transmission |
| title | Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation |
| title_full | Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation |
| title_fullStr | Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation |
| title_full_unstemmed | Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation |
| title_short | Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation |
| title_sort | enhancing performance and quality of transmission through knowledge driven machine learning based fwm mitigation |
| topic | FWM machine learning knowledge-driven DWDM multilevel regression quality of transmission |
| url | https://ieeexplore.ieee.org/document/10776958/ |
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